# AWS Glue
> AWS Glue is a serverless data integration service for preparing and moving data with Spark jobs, crawlers, and the AWS Glue Data Catalog. AWS Glue jobs can connect to MotherDuck through the MotherDuck Postgres endpoint using Glue's PostgreSQL JDBC support.
## How it works with MotherDuck

1. Create a MotherDuck access token.
2. Configure the AWS Glue job with a PostgreSQL JDBC connection to the MotherDuck Postgres endpoint.
3. Use `postgres` as the user, the MotherDuck token as the password, and `md:` or a specific MotherDuck database as the database name.
4. Use Glue's JDBC `dbtable` option for a table or view that the job should read.
5. Make sure the Glue job's network configuration can reach the public MotherDuck endpoint.

```python
connection_options = {
    "url": "jdbc:postgresql://pg.us-east-1-aws.motherduck.com:5432/md:?sslmode=require",
    "dbtable": "main.my_table",
    "user": "postgres",
    "password": "<motherduck_token>",
}

dyf = glueContext.create_dynamic_frame.from_options(
    connection_type="postgresql",
    connection_options=connection_options,
)
```

Use this route when a Glue job needs to read MotherDuck data as part of an AWS ETL workflow. For high-volume loading into MotherDuck, it is often simpler to write files to S3 from Glue and load those files from MotherDuck.

## Related content

- [View the full process in the AWS Glue JDBC documentation](https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-connect-jdbc-home.html)
- [MotherDuck Postgres endpoint](/key-tasks/authenticating-and-connecting-to-motherduck/postgres-endpoint/)
- [Loading data from S3 into MotherDuck](/key-tasks/loading-data-into-motherduck/loading-data-from-cloud-or-https)
- [Troubleshooting AWS S3 secrets](/troubleshooting/aws-s3-secrets/)


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